Association of Hypertension and Diabetes with Ischemic Heart Disease and Stroke Mortality in India: The Million Death Study
Bibliographic record
Abstract
Background: The cardiovascular outcomes of hypertension and diabetes in India have never been studied at the national level. Objectives: We conducted a nationally-representative proportional mortality study to measure the associations of hypertension and diabetes with premature mortality due to ischemic heart disease (IHD) and stroke among Indian adults. Methods: We determined causes of death by verbal autopsy from 2001-14 among 2.4 million households. We defined cases as those who died of the study outcomes and controls as those who died of injuries, respiratory causes, or cancer. We used multivariable logistic regression models to compute adjusted odds ratios (OR) measuring the association of hypertension and diabetes with IHD or stroke mortality, population-attributable fractions (PAF), and time trends. Results: The mean age at death was 55.6 (standard deviation 9.9) years for IHD, 58.2 (9.0) years for stroke, and 46.8 (injury) to 59.8 (respiratory) years for controls. There were more men among both the cases (IHD: 70.1%; stroke: 59.0%) and controls (injury: 76.6%; cancer: 55.4%; respiratory: 59.8%). Hypertension was associated with six- to eight-fold increases in the odds of IHD (OR 5.9, 99% CI 5.6-6.2) and stroke mortality (7.9, 7.4-8.5). Diabetes was associated with double the odds (1.9, 1.7-2.0) of IHD mortality and increased odds of stroke mortality (1.6, 1.4-1.7). Hypertension accounted for an increasing PAF of IHD mortality and decreasing PAF of stroke mortality. Diabetes was associated with relatively lower PAFs and variable time trends. Conclusions: Hypertension is associated with an unexpectedly high burden of cardiovascular mortality, and contributes to an increasing proportion of IHD deaths and a decreasing proportion of stroke deaths. Better management of hypertension and diabetes is urgently required to reduce premature cardiovascular mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".